A systematic review on sequence-to-sequence learning with neural network and its models
We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequenc...
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| Other Authors: | , , |
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2021
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| Online Access: | https://bspace.buid.ac.ae/handle/1234/2789 https://doi.org/10.11591/ijece.v11i3.pp2315-2326. |
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| _version_ | 1862980613530714112 |
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| author | Yousuf, Hana |
| author2 | Lahzi, Michael A. Salloum, Said Shaalan, Khaled |
| author2_role | author author author |
| author_facet | Yousuf, Hana Lahzi, Michael A. Salloum, Said Shaalan, Khaled |
| author_role | author |
| dc.creator.none.fl_str_mv | Yousuf, Hana Lahzi, Michael A. Salloum, Said Shaalan, Khaled |
| dc.date.none.fl_str_mv | 2021 2025-02-11T04:23:14Z 2025-02-11T04:23:14Z |
| dc.identifier.none.fl_str_mv | Yousuf, H. et al. (2021) “A systematic review on sequence-to-sequence learning with neural network and its models,” International Journal of Electrical and Computer Engineering, 11(3), pp. 2315–2326. 2088-8708 https://bspace.buid.ac.ae/handle/1234/2789 https://doi.org/10.11591/ijece.v11i3.pp2315-2326. |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | ProQuest Central |
| dc.relation.none.fl_str_mv | International Journal of Electrical and Computer Engineeringv11 n3 (Jun 2021): 2315-2326 |
| dc.subject.none.fl_str_mv | Connectionist temporal classifications Recurrent neural networks attention models Sequence-to-sequence models Systematic review |
| dc.title.none.fl_str_mv | A systematic review on sequence-to-sequence learning with neural network and its models |
| dc.type.none.fl_str_mv | Article |
| description | We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications. |
| id | budr_e5a81b5da8a2aa491931d20023ddca69 |
| identifier_str_mv | Yousuf, H. et al. (2021) “A systematic review on sequence-to-sequence learning with neural network and its models,” International Journal of Electrical and Computer Engineering, 11(3), pp. 2315–2326. 2088-8708 |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/2789 |
| publishDate | 2021 |
| publisher.none.fl_str_mv | ProQuest Central |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | A systematic review on sequence-to-sequence learning with neural network and its modelsYousuf, HanaLahzi, MichaelA. Salloum, SaidShaalan, KhaledConnectionist temporal classifications Recurrent neural networks attention models Sequence-to-sequence models Systematic reviewWe develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.ProQuest Central2025-02-11T04:23:14Z2025-02-11T04:23:14Z2021ArticleYousuf, H. et al. (2021) “A systematic review on sequence-to-sequence learning with neural network and its models,” International Journal of Electrical and Computer Engineering, 11(3), pp. 2315–2326.2088-8708https://bspace.buid.ac.ae/handle/1234/2789https://doi.org/10.11591/ijece.v11i3.pp2315-2326.enInternational Journal of Electrical and Computer Engineeringv11 n3 (Jun 2021): 2315-2326oai:bspace.buid.ac.ae:1234/27892026-01-29T15:03:22Z |
| spellingShingle | A systematic review on sequence-to-sequence learning with neural network and its models Yousuf, Hana Connectionist temporal classifications Recurrent neural networks attention models Sequence-to-sequence models Systematic review |
| title | A systematic review on sequence-to-sequence learning with neural network and its models |
| title_full | A systematic review on sequence-to-sequence learning with neural network and its models |
| title_fullStr | A systematic review on sequence-to-sequence learning with neural network and its models |
| title_full_unstemmed | A systematic review on sequence-to-sequence learning with neural network and its models |
| title_short | A systematic review on sequence-to-sequence learning with neural network and its models |
| title_sort | A systematic review on sequence-to-sequence learning with neural network and its models |
| topic | Connectionist temporal classifications Recurrent neural networks attention models Sequence-to-sequence models Systematic review |
| url | https://bspace.buid.ac.ae/handle/1234/2789 https://doi.org/10.11591/ijece.v11i3.pp2315-2326. |